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Artificial Neuron Modelling Based on Wave Shape

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Type
Published Article
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Identifiers
arXiv ID: 1403.1073
Source
arXiv
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Abstract

This paper describes a new model for an artificial neural network processing unit or neuron. It is slightly different to a traditional feedforward network by the fact that it favours a mechanism of trying to match the wave-like 'shape' of the input with the shape of the output against specific value error corrections. The expectation is then that a best fit shape can be transposed into the desired output values more easily. This allows for notions of reinforcement through resonance and also the construction of synapses.

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